Biometrics Innovation and Payment Sector Perception

Abstract

This paper presents an analysis of innovations in the biometrics market, which have started to play a very important role in personal identification and identification systems. The aim of the study was to analyze current customs and opinions regarding payment methods, as well as to identify threats and opportunities for new biometric solutions in this area. First, the history of the biometrics market is presented. Acceptance patterns of new technologies are explored and modified. The authors used literature reviews, qualitative research (focus groups), and quantitative research (questionnaire survey) as methods. The main value and importance of biometrics is the uniqueness of biometric patterns (e.g., face, fingerprint, iris, etc.), which takes the security of these systems to a new level. The results of the quantitative study based on the qualitative survey show positive verification of the hypothesized reasons; e.g., importantly, that the age of potential users of biometric payments influences the fear about personal data. Fear of losing personal data affects the perceived safety of biometric payments. Perceived security has a very strong influence on attitudes towards biometric payments, which is the strongest predictor of behavioral intention to use biometric payments.

1. Introduction

1.1. Introduction to Biometrics

Biometrics is the field that deals with measuring the characteristics of living organisms, including humans of course, but also animals and plants. At the basis of the application of biometrics in the field of authentication and identification lies a number of characteristics of living organisms, which allow the unambiguous distinction of an individual against the population. When choosing a particular biometric trait, we should be guided by the following criteria:

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Universality (almost every individual in the population has the trait);

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Unambiguity/uniqueness (the feature is highly distinguishable in the group);

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Persistence/variation over time (the trait does not degenerate over time);

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Technical feasibility of acquiring (the trait can be read fairly easily);

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Acceptability (cultural, religious concerns, sense of comfort and hygiene).

Biometric traits can be divided into physical/physiological and behavioral ones. Relating to the structure of individual parts of the body, e.g., iris patterns of the eye, the shape of a hand or ear, a fingerprint, face geometry or the shape of our veins, are the physiological features. Behavioral traits are developed and established during the maturation process of the individual and are related to their behavior, e.g., the way they walk, brain wave P300 (since brain waves have been proved to be unique enough across individuals to be used as biometrics), their handwritten signature (keystroking), or the characteristics of their voice (although sometimes the dual nature of this biometric is referred to). An example of a combination of both types of traits can be found in Figure 1.

Biometric security in the modern sense was born in the 19th century, thanks to the innovations of administrators, anthropologists, and French detectives [2]. The first known research publication on automated biometric recognition was Mitchell Trauring’s 1963 paper [3] in the journal Nature on fingerprint matching. The development of automated biometric systems based on other features, such as voice, face, and signature, also began in the 1960s. Subsequently, biometric systems based on features such as hand geometry and iris were developed. In this sense, almost 70 years have passed since the first paper on automated biometric recognition was published [4]. In Figure 2 there is a timeline of the development of the fingerprint; other biometric traits were investigated in quite similar timeframes in the 19th century.

Each biometric trait has its advantages, disadvantages, and limitations and these should be considered while selecting it for a given usage scenario. Due to the aforementioned characteristics of biometric traits, especially uniqueness, time invariability, and unambiguity, they should be considered as sensitive data and protected on multiple levels. The theft of raw biometric data can be used by an adversary to impersonate the victim and consequently gain unauthorized access or commit theft. The European Union, in its General Data Protection Regulation (GDPR), has addressed this issue and defined biometric data as particularly sensitive and has assigned to it the need for special protection [5]. Keeping the above in mind, raw biometric data should be treated with special care, which means primarily reducing its processing to a minimum. A fundamental solution to this issue is the use of so-called one-way processing [6], which generates from a sample of raw biometric data a template/profile/code that is an imprint of that data. Such a code still has individual properties and can be compared with others, so the idea of using biometrics is preserved. However, it is not possible to reconstruct the original biometric data only on its basis. This approach also protects the user in case the code database is leaked, because its use in a transformed form is negligible—similar to the case of password hashes. An example of iris encoding according to Daugman’s solution [7] can be found in Figure 3.

Referring to palmprints, biometric template protection methods, such as cancelable biometrics and a biometric cryptosystem, are essential to avoid direct disclosure of original palmprint features [8]. To strengthen user security, an approach based on so-called cancelable biometrics can also be used [9] in different types of biometrics. This solution is based on the ability to create multiple biometric identities from a single source data and to manage these identities. In a particular case, we can delete a given biometric identity and create another one based on its raw data. Such an application can be particularly useful when the database of the system in which the identity was used has been leaked or when we have a reasonable suspicion that someone is trying to impersonate that identity. When using the approach of cancelable biometrics, we usually need an additional source of external information beyond the biometrics itself so that we can modify the identity accordingly and combine it with biometric data. Such information can be, for example, a string of characters entered by the user, such as a password or PIN. Another existing method of template protection (used in palmprint biometrics) is a palmprint cryptosystem, which is the merging of biometrics and cryptography, which attempts to deploy biometrics as the authenticator of cryptographic applications, in which biometric features are claimed to be protected [8].

No matter how much effort is put into securing biometric data, at some point an adversary will manage to gain possession of it and try to use it to impersonate a legitimate user. This type of action is called a presentation attack, and the defense is presentation attack detection (PAD).

To counter such forgery attempts, liveness tests [10] are used, which are the basis of biometrics applications. A general scheme of how a biometrics-based security system works is shown in Figure 4. Liveness tests are designed to detect attempts to replace artificial objects—simulating real biometric features, including stolen biometrics converted into fake samples—photos, masks, recordings, etc. Any security system based on biometrics should place great emphasis on liveness testing and enforce high detection of substitution attacks. However, this is not an easy task as adversaries have increasingly more modern methods of defrauding at their disposal and are often extremely determined in demonstrating system weaknesses.

Applications of biometric solutions can be found in many fields, the most noteworthy being:

• Authentication (1:1);
• Identification (1:N);
• Access control (entry/exit registration);
• Continuous verification (real-time monitoring of biometric parameters);
• Biometric link (e.g., link between a person and an identity document).

Some of the abovementioned applications have already been addressed earlier and will be described in more detail later in this article. However, now we would like to focus on the last two applications that undeniably distinguish biometrics from other security solutions.

Biometrics, in contrast to standard authentication or authorization mechanisms (e.g., based on the knowledge factor–password or PIN or based on the possession factor–hardware token) allows the introduction of continuous verification of the identity of the person using the system [11]. Thanks to the transparent collection of such biometric data as in the rhythm of typing (keystroking), the way a touch screen is used, or the continuous analysis of the image from a camera, the system is able to verify, on an ongoing basis, whether an authorized person is still using its resources [12]. None of the standard mechanisms based on passwords or hardware keys provides such ease and efficiency of real-time verification while maintaining a high level of usability.

The second specific application of biometrics mentioned is the ability to create a biometric link between the person to whom the identity document has been issued and the person who is currently using it [13]. This is another layer of security that allows us to link an individual to a specific identity document through biometric characteristics. Without the biometric layer, we would only be able to verify that the data inside the chip of the identity document is consistent with the printed data or manually confirm the similarity of the photo with the person who holds the identity document. By using biometrics, we can not only verify the integrity of the data in the physical and electronic layers, but also apply an algorithm that authenticates biometric features and compares the biometrics of the person holding the document with the pattern recorded inside the electronic layer of the document. This solution will significantly reduce the effectiveness of counterfeiting identity documents and the use of documents by people to whom the documents have not been issued. Finally, let us look at iris biometry. The first smartphone with an LED, which allows for the scanning of the iris of the eye, has appeared on the market (so far only in Japan) as a form of camera protection. The Arrows NX F-04G phone by Fujitsu is sold by the Japanese telecommunication operator DOCOMO. The innovative LED is manufactured by OSRAM [14].

1.2. Market of Biometrics

Market forecasts indicate that the biometric systems market will be worth nearly USD 33 billion by 2022 [15]. The global biometrics market value will rise from USD 33 billion in 2019 to USD 65.3 billion in 2024 [16]. According to analyst firm Global Markets In-sights, the biometrics-based security solutions market will be worth USD 50 billion by 2024 [17]. From a total value of USD 23.4 billion in 2018, the global biometrics technology market is expected to reach USD 71.6 billion by 2024 [18]. By 2024, healthcare applications will register a compound annual growth rate (CAGR) of 26.3%, airport and seaport applications 25.8%, financial services 25.1%, and government services 23.3%. Retail, gaming, and hospitality applications will also see CAGR growth of 23% and 22.8%, respectively [18]. For information on stocks of companies using biometrics, see [19] (p. 3). Figure 5 represents the rise of worldwide biometric technologies market (Figure 5) [20].

The prominent key players in the biometric system industry are: SA (France), NEC Corporation (Japan), Fujitsu Ltd. (Japan), BIO-Key International, Inc. (U.S.), Precise Biometrics AB (Sweden), Secunet Security Networks AG (Germany), Thales SA (France), Aware, Inc. (U.S.), Cognitec Systems GmbH (Germany), and Cross Match Technologies (U.S.), among others [21].

Nearly six in ten people polled in the United States cited some hesitation or concern with biometric authentication. The top concerns among those polled by Statista included concerns about data and that the technology is too easy to fool. Biometric authentication includes fingerprints, face recognition, iris scanners, and any voice recognition. The government end-user sector is currently leading the market. It captured a significant market share of around 48% in 2018 [22]. North America leads the global biometrics market in 2018 with a market share of around 30%, which is followed by Asia-Pacific and Europe. In North America, the requirements of biometrics are higher because of the rise in the demand for developed security precautions and tourist administration after the 9/11 attacks. In North America, the biometrics market has witnessed strong growth over the years, especially in law enforcement, forensics, and government activities. Biometric passports became compulsory for issuance of foreign passports as of 2016 in the U.S. One of the strongest laws regarding biometrics exists in Illinois, named the Biometric Information Privacy Act (BIPA), which forbids companies from collecting information without prior consent from an individual. The Asia-Pacific is also a speedily evolving region. The existence of developing economies such as India, China, South Korea, Japan, Malaysia, Singapore, and Australia, which are displaying increased acceptance of biometrics, is no doubt driving market growth [23]. Compound annual growth rate (CAGR) of the global biometric market is shown in Figure 6.

1.3. Biometrics in Poland

The development of biometric technologies and popularization of their use in Poland has been very dynamic, as shown by numerous studies and reports. Poland is a receptive market for technological innovations, where large numbers of companies implement and experiment with the latest applications of biometrics.

A study by Visa [25] shows that as much as 62% of the surveyed Polish consumers declared their willingness to use biometrics instead of a password to verify payments. Consumers in Poland appreciate that thanks to biometrics, they do not have to remember passwords or codes: 73% of the surveyed consumers thought that using biometric data was faster than passwords and 76% thought it was easier, while 92% of Polish consumers who took part in the survey believed that fingerprint recognition was the safest form of payment authentication. According to the report Global TMT Predictions 2018 [15] prepared by Deloitte, by the end of the year 29% of smartphone users will verify their identity by fingerprint, and 42% of all mobile devices will be equipped with a fingerprint reader. Currently, one in four (25%) participants of the Mastercard 2019 survey [26] uses biometrics (not only for authentication and payment), but if they had the option, they would use it every second (50%). Forty-seven percent of Polish e-consumers would prefer biometric authentication for card payments, both online and in physical stores. Those who indicated preference for this method would most willingly use the technology of fingerprint recognition, iris scan, voice recognition, or facial features analysis. Confirmation of a transaction with a fingerprint was considered safe by every fourth respondent (75%) in the Mastercard study. This was a higher result than in the case of one-time codes (66%). More than half of the respondents considered the technology of facial features recognition (54%) or other kinds of biometrics (53%) was safe.

The Polish payment market is developing very dynamically and is one of the most modern in Europe. In general, it is a society that relies on cashless payments [27].

A survey conducted by Ping Identity [17] shows that 92% of companies believe that the use of biometric methods is a very effective way to authenticate people and increase the security of data stored in company resources. It found 86% of the respondents thought that biometrics allowed for good security as access to information is stored in the cloud. However, currently only 28% of companies use biometric systems in their local infrastructure and 22% use them to secure access to applications and data stored in the cloud.

In 2019 MasterCard conducted research in the context of Polish consumers’ attitudes towards online shopping, taking into account upcoming changes in e-commerce payments. The result of this work was a report called “Safe e-commerce”. The authors prove that biometrics will become a standard for confirming identity in payments. Moreover, more than 75% of the respondents believed that strong online card payment authentication, which came into force in mid-September 2019, was needed, which clearly sets a new trend in banking [28].

Therefore, there is a need to popularize authorization mechanisms such as biometrics, which are a convenient and effective way to confirm identity [28]. Similar conclusions were drawn in the results of research conducted by MasterCard in 2019.

At the same time, the authors of [28] observed that technologies such as biometrics, e-identity services or cashless payments are not something extraordinary for the respondents, but rather a desirable direction of development and providing greater convenience and usability of digital banking systems. The respondents showed great confidence in financial institutions and entrusted their data and money to them, so it is up to them to ensure the highest possible protection of consumers’ identity and accumulated assets.

PayEye (https://payeye.com/, accessed on 2 October 2020) is a Polish fintech that introduced the world’s first such secure, convenient, and complete payment based system, for both payment acceptance and user identification, using iris biometrics. By combining technology with science, PayEye has created a whole, independent, and secure ecosystem, which consists of proprietary, innovative eyePOS terminals, an electronic wallet for users, algorithms which convert the iris into a biometric pattern and, in the future, also solutions for e-commerce.

1.4. Literature Review of Technology Acceptance Models

As mentioned previously in the paper, biometrics play a crucial role in many innovative systems. Each innovation is subject to the implementation process and as a result is or is not accepted by market participants. Many researchers emphasize that diffusion is a social process that occurs among people in response to learning about an innovation such as a new evidence-based approach for extending or improving health care. In its classical formulation, diffusion involves an innovation that is communicated through certain channels over time among the members of a social system [29,30]. Market practice aspects and research paradigms known as the diffusion of innovation (DOI) [31] can be applicable into the complex context of biometrics identification processes and its usage in payment systems.

Innovative biometric systems that incorporate biometric payments are rapidly becoming an important part of information technology (IT) and information systems (IS). The literature indicates that biometrics is becoming the standard of modern life, as commercial and governmental entities are rapidly adopting technology that promises increased security and better identification [32] (p. 314). Theoretical frameworks for technology acceptance are IS theories that model how users accept and use a particular technology. These theories suggest that when users are introduced to a new technology, many factors influence their decision about how and when they will use it [33].

It has been noted in the literature that the acceptance and use of information technology has been one of the priority issues in the research of information systems and practice since the late 1980s [34,35]. Building on the theory of reasoned action (TRA) formulated earlier by Fishbein and Ajzen [36], Davis [37] developed the technology acceptance model (TAM) and introduced it to the IS field. TRA has its roots in social psychology and attempts to explain why individuals engage in consciously intended behavior. In TAM, a user’s motivation to adopt a new technology can be explained by three constructs: perceived ease of use (PEU), perceived usefulness (PU), and attitude towards using the system [38].

IS and IT are becoming increasingly complex and crucial for business operations, thus making the issue of acceptance an important challenge in IT implementation [33]. Many models and theories have been introduced that examine the acceptance and use of information systems from past to present. The unified theory of acceptance and use of technology (UTAUT) is a model that explains the use of technology by 70% of society. It is also used to estimate the probability of success of a new technology and to evaluate the adoption of various technologies [39,40].

In the study of Venkatesh et al. [39], UTAUT comprises of four main factors. These are performance expectancy, social influence, effort expectancy, and facilitating conditions. In addition, UTAUT includes four intermediate individual variables, gender, age, experience, and voluntariness of use, which predict the relationship between primary factors and behavioral intention and use behavior. According to UTAUT, there are determining factors that directly affect intention or use in models combined within the UTAUT framework. These determining factors are called performance expectancy (PE), social influence (SI), effort expectancy (EE), and facilitating conditions (FC). According to the literature review, the FC are empirically identified as the direct determinant of adopting the behavior. These factors play a prominent role as direct determinants of user acceptance and usage behavior [40].

Part of this complexity of the acceptance issue in biometrics, especially in the con-text of payments, is the issue of security and privacy. Langenderfer and Linnhoff [32] in their work analyze the costs and shed light on how biometrics can negatively affect consumers. The authors point out that the rapid development of biometric authentication technology represents a double-edged sword for consumers. On the one hand, increased use of biometrics is likely to reduce identity theft, improve consumer convenience by eliminating or reducing the use of passwords, and lower prices by reducing fraud costs for retailers. On the other hand, while overall security is likely to be enhanced, security breaches will be more costly and require significantly more effort to remedy.

The level of security perception in the context of biometrics, as a matter of the individual characteristics, is strongly connected with the privacy issue. A wealth of existing theoretical work has suggested that privacy levels, along with privacy perceptions, regulation behaviors, and information disclosure, are inherently context-dependent and vary across situations [41,42]. As Masur [41] (p. 312) points out, “privacy is a subjective perception resulting from the characteristics of the environment in which an individual happens to be at a given time”.

It is also important to emphasize that research in IS has investigated the differences in levels of privacy concerns and their impact on a number of dependent variables such as willingness to provide information and intention to transact online [42,43,44]. Smith et al. [45], in their interdisciplinary review of privacy research, summarized existing privacy research into the antecedent–privacy concern–outcome (APCO) framework of information privacy, with privacy concerns as the central element, accompanied by antecedents and outcomes. Scientists also suggest that further research on the identification of the factors that contribute to privacy concerns is essential.

Several antecedents of privacy concerns have been found by Li [46] in the process of systematically reviewing existing empirical studies on privacy. The list of factors contains: (a) individual factors (demographics, personality traits, knowledge and experience, self-efficacy), (b) social factors (e.g., social norms), (c) organizational factors (privacy policies, website informativeness, company reputation), (d) macro-environmental factors (culture, regulatory structures), and (e) information contingencies (information sensitivity, type of information) [43,46,47]. Li [46] points out that for some factors (e.g., privacy experiences having a positive impact on privacy concerns), results have been cross-validated across studies, while for others (e.g., internet use and fluency and the big five personality traits), results have been inconsistent. Therefore, the researchers indicate that it is essential to conduct further research to examine the impact of different antecedents on privacy concerns [42].

Drawing on elements of DOI, the technology acceptance model (TAM), and a unified theory of acceptance and use of technology (UTAUT) along with the trust−privacy research field, Miltgen et al. [33] proposed an integrated approach that is both theoretically and empirically grounded. Their study examines individual acceptance of biometric identification techniques in a voluntary environment, measuring the intention to accept and further recommend the technology resulting from a carefully selected set of variables (Figure 7).

Research [33] confirms that the influence of known technology acceptance variables, such as compatibility, perceived usefulness, and facilitating conditions, on the acceptance of biometric systems and subsequent recommendations. Second, antecedent factors such as privacy concerns, trust in technology, and innovation also prove to be influential. Third, if not innovation, the most important factors explaining the acceptance and recommendation of biometric systems do not come from traditional adoption models (TAM, DOI, and UTAUT) but from the trust and privacy literature (trust in technology and perceived risk).

Miltgen et al. [33] in their paper pointed out that there are many other external factors that may influence responses that should be considered and investigated in the future, such as: ‘security perceptions of users of biometric systems’, ‘consumer characteristics’, ‘situational factors’, ‘product characteristics’, and ‘previous experiences’. The authors suggest that additional future research should investigate these ‘other’ factors and their impact on consumers’ behavioral intentions to accept new technologies in general and biometrics in particular.

On the basis of literature studies (both scientific literature and a review of journals, magazines, market reports), a research gap was identified. This gap concerns the need for further exploration of consumer attitudes towards biometrics, with particular emphasis on the use of iris biometrics in payment systems.

2. Materials and Methods

2.1. Methodology of the Qualitative Research

After reviewing the literature, we decided to use a qualitative research method to explore the research topic from the perspective of a defined scientific gap.

The aim of the study was to analyze current customs and opinions regarding payment methods, as well as to identify threats and opportunities for new biometric solutions in this area. Based on the process of studying literature, as well as the author’s own observations regarding the biometric market and its participants, we were able to formulate research questions:

• What is the current situation referring to payment solutions and its image in the minds of customers?
• What is the image and opinion of biometrics from the perspective of its usage in payment systems?

As a method of qualitative studies, we chose the FGI (focus group interview)—a well-known technique for collecting data in social sciences, which consists of conducting collective in-depth (semi-structured) intelligence in groups of 4–9 people (depending on the research area and organizational capabilities). We decided to focus on the newest form of biometric usage, which is iris-based biometrics (for now only available in Poland as a pilot project). We decided to choose this particular case because of its innovative nature and the effect of fresh opinions. A total of 4 focus groups meetings were conducted (November−December 2019), the shortest of which lasted 2.5 h and the longest almost 4 h. The participants were of different age groups, from different localities (both residents of small towns and big cities), and of different ages (the youngest was 17 and the oldest 74).

• Focus I—4 women and 5 men predominate in focus groups;
• Focus II—3 women and 3 men;
• Focus III—5 women;
• Focus IV—4 women and 3 men.

In conclusion, the gender differences in the focus studies conducted were as follows: 16 women and 11 men, for a total of 27 people. They were from different professions and lifestyles—high school students, housewives, secretarial managers, accountants, office workers (4 participants), sales, lawyers, entrepreneurs (3 participants were from businesses). All groups were surveyed without showing any type of biometric devices (no biometric payment tools).

According to the focus group participants, there were no apparent flaws in the current payment system (notably, this generation uses a card and a smartphone). There was enthusiasm about using smartphones as a payment option, especially among the younger generation. All participants stressed that biometric solutions are associated with risks of data leakage, health risks, “data hacking”, etc. The visualization of the most frequent word used in relation to eye biometrics during the focus research, marked by emotion, was definitely the word “fear”.

After the focus study, without the demonstration of a biometric device (iris-based), two focus studies with the demonstration of an identification device were conducted. After observing the first set of focus groups, we decided to divide the subsequent groups according to gender categories, as we noticed that due to the stereotype of a wider knowledge of technological issues in the male group, women kept their opinions to themselves and were not very open to sharing their thoughts. Due to this, the first meeting was a women-only meeting. These women represented different ages and professional categories:

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Economics student—master’s degree;

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Employee of a creative agency;

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So-called housewife, however, conducting business;

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Accountant;

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Cashier;

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Management student;

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Bachelor’s degree/at the same time waitress;

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Administrative employee of a small company (staff).

The age gap of the participants of the study (focus group) was 21–56 years old.

The first phase of the study looked similar to previous audio meetings: the issues of making payments, stressors, annoying and unpleasant payments were discussed (mainly queue times, breathing on the back of other customers, and lack of hygiene, whether using cash or card, “system jams”, “internet crashes”, and similar problems).

Participants declared that they mostly paid by card (plastic or smartphone) or cash. Another element of discussion was biometrics and their approaches to the use of biometric solutions in payments. In this area, there was a fairly strong element of doubt about biometric solutions, with survey participants associating them with gaining access to their accounts, and therefore lack of security surveillance, the possibility of copying fingerprints, and more than half of the survey participants stated (which was also important in this part) that they did not use biometric features at all and usually paid by card or cash, and used a code to identify themselves on their laptop or smartphone.

In the next part of the study, biometric equipment was presented. After a brief presentation, participants were encouraged to “encode” their eye on the device, which they were rather reluctant to do and even stressed about whether something could happen to their eyes.

In the next round, participants were able to check how the equipment recognized their iris. This step was welcomed more positively, although it must be said it was not met with great enthusiasm.

The final element was a discussion of other possible uses of the equipment. It is worth noting that participants strongly emphasized the health aspect—concerns were raised about the impact of eye scanning on health.

After the study, which took place with a female group, only male participants were intentionally invited to the second meeting. The format of the second focus group (except for different participants) was identical to the format of the stages (including the presentation of the device) in the female group. A total of eight participants, aged 21–54, representing different professions and industries took part in the second study:

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Aviation/own business;

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Film production;

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Economics student;

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Management student/bank employee;

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Management representative;

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Marketing director in a large international production and trading company;

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Construction worker;

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Rock musician.

There were noticeable differences in the observations in this male group compared to the female group, the most important being:

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No strong emphasis on health and hygiene aspects;

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No problem with searching for their wallet, phone, card in their pockets (for objective reasons) as aspects of everyday life.

2.2. Methodology of the Quantitative Research

A survey was conducted in order to verify and extend the main results of the qualitative research and the observed dependencies. A questionnaire was prepared during a brainstorming session based on the results of qualitative research and the authors’ own observations. A pilot survey was used to verify this model. The sample size was planned for a minimum of two hundred respondents, and social platforms were selected as the distribution channel for the questionnaire. For the pilot study, the respondents were not selected randomly, but the aim was to find representatives of working age for the sample. A list of analyzed items in the questionnaire is presented in the Appendix A of

Table A1. Questionnaire items used in quantitative analysis.

No. Question	Answering
How important are these payment methods in your daily purchases? [cash]	5 point Likert-type scale
2. How important are these payment methods in your daily purchases? [card]	5 point Likert-type scale
3. How important are these payment methods in your daily purchases? [mobile]	5 point Likert-type scale
4. How important are these payment methods in your daily purchases? [biometric]	5 point Likert-type scale
5. Do you what biometric payment is?	yes/no
6. Have you ever used biometric payments?	yes/no
7. What kind of biometric payments have you used [a: finger, b: face, c: eye, d: voice]?	selection
8. What is your attitude towards biometric payments?	5 point Likert-type scale
9. Do you think biometric payments are safe?	5 point Likert-type scale
10. What are your main concerns about biometric payments? [how data is stored]	5 point Likert-type scale
11. What are your main concerns about biometric payments? [how data is used]	5 point Likert-type scale
12. What are your main concerns about biometric payments? [possibility of personal data theft]	5 point Likert-type scale
13. What are your main concerns about biometric payments? [lack of legal regulations]	5 point Likert-type scale
14. What are your main concerns about biometric payments? [technological errors (e.g., data not detected)]	5 point Likert-type scale
15. What are your main concerns about biometric payments? [low number of sites accepting biometric payments]	5 point Likert-type scale
16. What are your main concerns about biometric payments? [lack of knowledge about biometric payments]	5 point Likert-type scale
17. What are your main concerns about biometric payments? [discomfort with using biometric data in public]	5 point Likert-type scale
18. Would you like to use biometric payments on a daily basis?	yes/no
19. What kind of biometric payment system would you like to use? [a: finger, b: face, c: eye, d: voice]?	selection
20. Do you think biometric payments will replace current payment methods?	4 point Likert-type scale
21. Which of these factors do you think are driving the use of biometric payments? [security]	5 point Likert-type scale
22. Which of these factors do you think are driving the use of biometric payments? [ease of use]	5 point Likert-type scale
23. Which of these factors do you think are driving the use of biometric payments? [no need to remember passwords and pin numbers/no]	5 point Likert-type scale
24. Which of these factors do you think are driving the use of biometric payments? [it is not possible to lose the identification method]	5 point Likert-type scale
25. Which of these factors do you think are driving the use of biometric payments? [environmental friendliness]	5 point Likert-type scale
26. Which of these factors do you think are driving the use of biometric payments? [speed of service]	5 point Likert-type scale
Demographics
27. Age	number
28. Gender [female, male, other, not to show]	selection
29. Education [primary, secondary, vocational, tertiary, higher]	selection
30. Occupational status [working, jobless, student, retired]	selection
31. Monthly household income per person (approximate net amount)	number

.

The statistical analysis was based on measures of dependence between the variables: mainly on the correlation matrix, and also on cross tables, e.g., for binary variables. Exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) were used for the groups of questionnaire items, and for the variables grouped by EFA, a reliability analysis was performed using the Cronbach’s alpha coefficient. Dependencies between constructs based on a linear correlation matrix were analyzed using path analysis within a structural equation model (SEM) to verify the significance of direct and indirect causality.

Due to the indicated research area, on which the research presented in this article focuses (biometric innovations in the payment sector), the model of hypothetical dependencies was based on the analysis of literature studies, including the presented UTAUT model, as well as qualitative research conducted by researchers. Such research procedures made it possible to derive and consequently define a new, proprietary model of hypothetical dependencies, which is presented in Figure 8. Our own observations and conducted qualitative research on the perception of biometric payments using the iris of the eye (BP) allowed us to prepare a set of hypotheses that lead to the acceptance model of BP verified in this study. The observation about the strong influence of consumer age on BP acceptance is due to the fact that age is the main exogenous variable in the first stages of diffusion of this type of innovation. Thus, age is not only an impact moderator, but also a primary predictor. The main target variable is the behavioral intention to accept and use BP (but not yet recommend BP due to the novelty of the technology), as described in the literature models.

During the process of discussion, we formulated direct causes between variables, marked with arrows in Figure 8. The directions of the relationships between the variables of the model are presented in

Table 1. Set of hypotheses in verified model.

Symbol of Hypothesis	Content of Hypothesis
H1a	The higher the age of the respondents, the greater their fear of using personal data in BP systems
H1b	The higher the age of the respondents, the greater their fear of barriers in life with BP
H1c	The higher the age of the respondents, the greater their knowledge and experience (also in BP area)
H2a	Fear about personal data has a direct negative effect on perceived safety
H2b	The higher the respondents’ concerns about personal data, the worse their perception of use and facilitating conditions of BP
H3a	Fear of barriers in life has a direct negative effect on perceived use and facilitating conditions
H3b	Fear of barriers in life has a direct negative effect on attitude towards BP
H4a	Knowledge and experience has a direct positive effect on perceived use and facilitating conditions
H4b	Knowledge and experience has a direct positive effect on importance of innovative payments
H4c	Knowledge and experience has a direct positive effect on replacement in future
H5a	Perceived safety has a direct positive effect on importance of innovative payments
H5b	Perceived safety has a direct positive effect on attitude towards BP
H6a	Perceived use and facilitating conditions variable has a direct positive effect on attitude towards BP
H6b	Perceived use and facilitating conditions variable has a direct positive effect on behavioral intention to accept and to use BP
H7a	Importance of innovative payments has a direct positive effect on attitude towards BP
H7b	Importance of innovative payments has a direct positive effect on behavioral intention to accept and to use BP
H8a	Replacement in future has a direct positive effect on importance of innovative payments
H8b	Replacement in future has a direct positive effect on behavioral intention to accept and to use BP
H9	Attitude towards BP has a direct positive effect on behavioral intention to accept and to use BP

together with the content of the hypotheses.

The measurements of variables were constructed from questions presented in the Appendix A of Table A1. Predicted behavioral intention to accept and to use BP was measured as the sum of binary items numbered 18 and 19a–19d from Table A1. Three variables are measured with only one question:

(1)

Self-reported perceived safety (item 9) was understood as the inverse of perceived risks from the model shown in Figure 7;

(2)

Self-reported attitude towards BP (item 8) was understood as one of the main mediators predicting behavioral intention to accept and to use BP, rather shaped by other predictors and not initially trust (component of the model) presented in Figure 7;

(3)

Replacement in future (item 20) was a stated belief about payments that may also be shaped by unmeasured science fiction in literature and films.

The three variables from the UTAUT model (perceived ease of use, perceived usefulness, and facilitating conditions) were considered during the preparation of the questionnaire as combining into a single latent variable called perceived use and facilitating conditions, measured as the sum of items 21–26 from Table A1. Importance of innovative payments was measured by the sum of items 3 and 4; not only biometric payments in their initial use in society (in real shops), but also mobile payments (e.g., codes generated in banking applications used mainly online in online shops), contrasting with traditional cash payments and the very popular card payments. Fear about personal data was measured as the sum of items 10–12 from Table A1 and is understood as concern about data privacy from Figure 7. Fear of barriers in life was measured as the sum of items 13–17, understood as social problems (the inverse of social influence from the UTAUT model or social incompatibility used as inverse in the DOI theory shown in Figure 7). Knowledge and experience was measured as the sum of binary items 5 and 7a–7d and shows respondents’ acquired knowledge of BP. By constructing the model shown in Figure 8, discussing what is cause and what is effect, the direction of the hypotheses shown in Table 1 was decided. Comparing Figure 8 with the model shown in Figure 7, the only thing missing from the model in Figure 8 is the perceived innovativeness of biometrics, but this was evident for payments. Statistical software Statistica 13.3 [48] was used to analyze the collected survey data to assess the measurement of latent variables and to verify the hypotheses.

3. Results

3.1. Qualitative Research

From a consumer’s perspective, biometric authentication offers many advantages. Once enrolled in a biometric system, the customer is instantly untroubled by the fraudulent use of their credit cards. Payments can be easily made without carrying any cash or other forms of identifiers and in this case the only thing required is their fingerprints. They can be certain that if their car or computer is stolen, it will be worthless to all except the most sophisticated thieves since access is biometrically controlled; in consequence this leads to a decrease in the impetus for theft. The bothersome task of remembering passwords could be considered a thing of the past.

As the general conclusions of the preliminary qualitative studies carried out, we point out the skeptical approach to biometric solutions in payment systems.

It is necessary in the future to indicate market concerns about novelties, but to seek innovation to assuage numerous consumer concerns about the introduction of biometric applications with important implications for marketing communication associations with nature, simplicity, and naturalness. There is a need to promote biometric solutions in the educational form.

3.2. Quantitative Research

Continuing with the qualitative research, results were collected from the questionnaire. Most analyses (EFA, CFA, reliability analysis, SEM) were based on a linear correlation matrix where most values are significantly different from zero (hard to publish because of matrix size, but available upon request from the corresponding author). The number of observations was 200, which is not a very large number to verify such complex hypothesis models. The age of the respondents was the only objective variable analyzed, the others being subjective or behavioral variables. The mean age was 28.7 years, and the standard deviation was 9.2. The respondents were rather young people: the minimum age was 17 years, and the maximum was 61 years, so the sample included representatives of almost the entire working age range.

Factor analysis of variables with Likert-type response scales confirmed the three measurement scales for fear concerning personal data, fear of barriers in life, and perceived use and facilitating conditions developed from the questionnaire items in Table A1; e.g., the EFA scree plot reduced the dimensions to three and the CFA fit was rather good.

Following the strict methodology of Song et al. [49,50,51,52], before proceeding to testing the hypotheses H1–H9, we checked the reliability of scales and measurement items.

3.3. Reliability and Validity

As a measure of reliability, that is, the internal consistency of the measurement items of the survey, we used Cronbach’s alphas, as given in

Table 2. The internal consistency of the measurement items.

Construct	Cronbach’s Alpha	Variable	Standardized Factor Loading	SMC	AVE	Composite Reliability
Fear about personal data	0.879	Q10	0.877	0.687	0.761	0.905
		Q11	0.910	0.731
		Q12	0.828	0.538
Fear of barriers in life	0.829	Q13	0.681	0.492	0.569	0.868
		Q14	0.780	0.439
		Q15	0.678	0.411
		Q16	0.851	0.546
		Q17	0.767	0.500
Perceived use and facilitating conditions	0.879	Q21	0.708	0.473	0.627	0.909
		Q22	0.837	0.649
		Q23	0.782	0.567
		Q24	0.793	0.532
		Q25	0.775	0.481
		Q26	0.848	0.606

. All the values were above the threshold 0.7 (the minimum value was 0.830); that is, the scale may be regarded as reliable.

We also investigated the correspondence between the constructs and their operationalization. This constitutes four components: analysis of unidimensionality, convergent validity, discriminant validity, and nomological validity.

To investigate the unidimensionality of the scale, we performed CFA to examine whether the indicators were assigned to the constructs adequately. Using the maximum likelihood method of estimation, we obtained a satisfactory result, including fit indices, presented in

Table 3. Goodness of Fit Test.

Category of Index	Measure	Value
Absolute fit indices	Chi-square	152.381
	d.f.	74
	Chi-square/d.f.	2.059
	GFI	0.905
	AGFI	0.863
	RMSEA	0.071
	SRMR	0.054
Incremental fit indices	NFI	0.894
	IFI	0.948
	TLI	0.930
	CFI	0.942

. As chi-square/d.f., it should range between 1 and 5, so our result fitted well within that. GFI and AGFI should exceed 0.9; the latter in our research was a bit below this threshold. RMSEA should range between 0.05 and 0.08, and the value for our research fitted well. SRMR, which should be below 0.08, equaled 0.054 and was below the threshold. Incremental fit indices, NFI (normed fit index), IFI (incremental fit index), TLI (Tucker–Lewis index), and CFI (comparative fit index) were above the threshold of 0.9 apart from NFI, which is slightly below. Overall, we regard the result of this investigation as satisfactory to accept the unidimensionality of the scale.

As for convergent validity, which we present in

Table 4. Correlation matrix of the constructs.

Construct	AVE	F1	F2	PU
Fear about personal data (F1)	0.761	1
Fear of barriers in life (F2)	0.569	0.456 ***	1
Perceived use and facilitating conditions (PU)	0.627	−0.020	0.095	1

Note: *** p < 0.001.

, standardized factor loadings should be above a 0.5 threshold, and all were well above this (the minimum was equal to 0.678), while AVE (average variance expected) should also be above 0.5, and within our research all values were above this. Thus, we may conclude that the convergent validity is acceptable.

For discriminant validity, we calculated the correlation coefficients between constructs (presented in Table 4). Squares of those values should not exceed the minimum AVE. The only statistically significant coefficient of correlation, 0.456, was low enough and its square (0.208) was much lower than the minimum AVE (0.569). Thus, discriminant validity is satisfactory.

Nomological validity refers to possible collinearity and mutual dependencies of constructs. As the highest correlation coefficient was not too high, we did not expect this effect; still, we calculated variance inflation factors (VIF) to check whether they were below the commonly used threshold of 10. All values were well below it (between 1 and 2); thus, we conclude that the nomological validity of our research is acceptable.

The reliability coefficient for behavioral intention to accept and to use BP was 0.719 and was sufficient—the Kuder-Richardson Formula 20 (KR-20) for binary variables is equivalent to Cronbach’s alpha). However, the reliability of the knowledge and experience measure was insufficient as the KR-20 was equal to 0.419 for the sum of items 5 and 7a–7d from Table A1 and 0.489 for the sum of items 5–6 (overall experience with BP). A new research question arises: is it possible to combine two characteristics (1) knowledge and (2) experience into one variable? Inferences about knowledge and experience may be distorted by random measurement error; also, the reliability of importance of innovative payments measurement was insufficient: KR-20 was equal to 0.448, so conclusions about this variable may be biased by random error.

The hypotheses in Table 1 were verified in a structural equation model, and its parameters together with the conclusions regarding the hypotheses are presented in

Table 5. Estimated SEM parameters, their p-values, and conclusions about hypotheses.

Symbol of Hypothesis	Parameter Estimate	p-Value (Rounded to 3 Digits)	Conclusion about Hypothesis
H1a	0.323	0.000	Verified
H1b	0.171	0.013	Verified
H1c	−0.083	0.237	Rejected
H2a	−0.241	0.000	Verified
H2b	−0.067	0.319	Rejected
H3a	0.175	0.008	Rejected because of positive value
H3b	−0.124	0.020	Verified
H4a	0.258	0.000	Verified
H4b	0.348	0.000	Verified
H4c	0.262	0.000	Verified
H5a	0.126	0.053	Almost verified
H5b	0.589	0.000	Verified
H6a	0.205	0.000	Verified
H6b	0.116	0.052	Almost verified
H7a	0.149	0.005	Verified
H7b	0.200	0.001	Verified
H8a	0.052	0.445	Rejected
H8b	0.179	0.002	Verified
H9	0.395	0.000	Verified

. The fit of this model was not sufficient (SRMR = 0.176, RMSEA = 0.175, GFI = 0.843, AGFI = 0.668, NFI = 0.650, CFI = 0.670), so this complex model should be improved or simplified. However, the estimated parameters and their p-values when tested equal to zero are reason to make preliminary inferences about the hypotheses that make up the model shown in Figure 8.

Most of the hypotheses have been positively verified (p-value less than 0.05), some of them forming a cause and effect sequence: e.g., age had a positive effect on fear about personal data. Fear about personal data had a negative effect on perceived safety. Perceived safety had a very strong positive effect on attitude towards BP, which was the strongest predictor having a positive effect on behavioral intention to accept and to use BP. Hypotheses in which the p-value of the estimated parameter was very close to the significance level of 0.05 were classified as “almost verified”. Three hypotheses were rejected due to the insignificance of the parameter estimate (not significantly different from zero). The rejection of H3a is very interesting because the sign of the estimated parameter was opposite to the hypothesized one—a reason to investigate the relationship between the measured variables. Positive verification of the hypotheses can also provide a basis for recommendations on how to manage BP to gain greater acceptance and use in society, alleviating various concerns and balancing them with utility and facilitating conditions.

4. Discussion and Conclusions

Respondents have begun to understand the need to use biometrics, trust it more, and appreciate the benefits it brings. It can be inferred from the responses that users feel the need to increase the level of security. The most common indications were greater use of biometrics, such as fingerprints and iris scans. Such solutions inspire confidence in respondents, regardless of their age and experience in e-banking. This was indicated by both younger and older people. Based on some of the interviews, there is also an image of a person for whom convenience is definitely more important than security. Such a person would gladly give up, for example, confirming actions with SMS codes. This opens the path for the popularization of biometric solutions [53]. However, the connection to new emotional aspects of BP is similar to the connection of psychological aspects to TAM and UTAUT, as presented in work of Koufaris [54] on online shopping.

Reading the results of recent studies in the BP sector shows a similar importance of the attitude variable, shaped by other predictor variables, such as in the study of behavioral intention to use BP reviewed by Moriuchi [55]. Such an important role is played by the attitude variable in the presentation of the research conducted by Rosén et al. [56]. In the research model used by Zhang and Kang [57], perceived usefulness plays a similar role as a mediator in predicting intention to use BP, but also safety is very important, as are concern for personal information and perceived safety in the research model reviewed in this article. The inverse of safety (perceived risk) also plays an important role in the BP model verified by Liu and Tu [58]. In the work of Hizam et al. [59], social influence and perceived system quality are added to TAM as predictors—good functioning of BP systems is also measured by perceived user conditions and facilitation, as well as fear of barriers in the model verified in this paper.

Our study has many limitations to generalize the results—the sample is only from Poland and is rather too small to verify such a complex model of behavioral intention to use BP. New analyses could be conducted on the basis of this data, e.g., investigating the influence of gender on emotional variables such as fear about personal data or perceived safety, or their moderating role in predicting intention to use BP. Future research is planned on a more representative random sample of working-age members of the public (average age is likely to be much higher), with a minimum number of respondents of one thousand. The complex model will also be simplified, and some analysis should be applied so that the model with direct and indirect causes is better suited to predicting intention to use BP. The main results of this pilot study should be verified in new analyses. The measurement of some constructs (especially without sufficient reliability) could be improved by using measurement scales tested in the literature. Biometric data is so important that consumers are very often unsure whether they can terminate their agreed use of BP and remove their own data from the consumer database. Compared to other technologies, biometrics combines technological innovation with biology and is even similar to medical technologies. Fear about personal data as one of the important variables can be the basis of a cluster analysis of potential consumers using BP, carried out to place the BP market in the BP-open part of society. Conspiracy mentality [60] should also be measured as one of the reasons for avoiding BP. Well measured general openness (or curiosity) to new and innovative technologies could also be added to the models. The research procedure made it possible to derive and define a new, proprietary model of hypothetical dependencies, which is presented in the paper, that along with the analysis and systematization of knowledge of the biometric market, as well as undertaking innovative research in the field of the payments market using biometrics, should be considered the main contribution of this article.